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International Conference on Business Information Systems

BIS 2019: Business Information Systems pp 233–244Cite as

Re-engineering Higher Education Learning and Teaching Business Processes for Big Data Analytics

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 354))

Abstract

Big Data analytics need to be combined with higher education business processes to improve course structure and delivery to help students who have struggled to stay in the course by identifying their engagement and correlation with different variables such as access to documents; assignment submission etc. Online activity data can be used to keep students on track all the way to graduation and universities struggling to understand how to lower dropout rates and keep students on track during their study program. In this paper we discuss how Big Data analytics can be combined with higher education business processes using re-engineering for structured data, unstructured data, and external data. In order to achieve this objective, we investigate the core business processes of learning and teaching and define a re-engineered higher education business process model.

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Author information

Authors and Affiliations

  1. Central Queensland University, Sydney, NSW, 2000, Australia

    Meena Jha & Sanjay Jha

  2. Home Affairs, Canberra, ACT, 2609, Australia

    Liam O’Brien

Authors
  1. Meena Jha
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  2. Sanjay Jha
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  3. Liam O’Brien
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Corresponding author

Correspondence to Meena Jha .

Editor information

Editors and Affiliations

  1. Poznań University of Economics and Business, Poznań, Poland

    Witold Abramowicz

  2. ETSI Informática, University of Seville, Seville, Spain

    Rafael Corchuelo

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Jha, M., Jha, S., O’Brien, L. (2019). Re-engineering Higher Education Learning and Teaching Business Processes for Big Data Analytics. In: Abramowicz, W., Corchuelo, R. (eds) Business Information Systems. BIS 2019. Lecture Notes in Business Information Processing, vol 354. Springer, Cham. https://doi.org/10.1007/978-3-030-20482-2_19

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  • DOI: https://doi.org/10.1007/978-3-030-20482-2_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20481-5

  • Online ISBN: 978-3-030-20482-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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